Backbones: As shown in Table REF  and Table REF , previous methods utilize different backbone networks (, VGG16, RestNet50, ResNet101, etc.) pre-trained on ImageNet [1]} to extract visual features.Stronger ones usually bring better performances. For example, in DB [2]}, using ResNet50 with deformable convolution networks (termed as `ResNet50-DCN') as the backbone has the improvement of 2.4% in \(\textit {F-measure}\) , compared with ResNet18-DCN. Similarly, using ResNet101 also increases the \(\textit {F-measure}\)  of 1.2% than using ResNet50 in TextFuseNet [3]}. Even though some works adopt the same backbone networks, different versions of networks may also influence the performance. For example, PAN [4]} uses the ResNet-v2 [5]}We found this from their official code: https://github.com/zhongqianli/pan_pp.pytorch while other methods usually adopt ResNet-v1 [6]}.
